docstr: :param y: the output of the bijection
  :type y: torch.Tensor
  Inverts y => x. As noted above, this implementation is incapable of
  inverting arbitrary values `y`; rather it assumes `y` is the result of a
  previously computed application of the bijector to some `x` (which was
  cached on the forward call)
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code: def _inverse(self, y):
      """
      :param y: the output of the bijection
      :type y: torch.Tensor
      Inverts y => x. As noted above, this implementation is incapable of
      inverting arbitrary values `y`; rather it assumes `y` is the result of a
      previously computed application of the bijector to some `x` (which was
      cached on the forward call)
      """
  
      raise KeyError(
          "ConditionedRadial object expected to find key in intermediates cache but didn't"
      )
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 4 for calling the ConditionedRadial obj's underlying member method code with example usage: obj._inverse(y) without return types